Automated T-wave analysis can differentiate acquired QT prolongation from congenital long QT syndrome.

BACKGROUND: Prolongation of the QT on the surface electrocardiogram can be due to either genetic or acquired causes. Distinguishing congenital long QT syndrome (LQTS) from acquired QT prolongation has important prognostic and management implications. We aimed to investigate if quantitative T-wave analysis could provide a tool for the physician to differentiate between congenital and acquired QT prolongation. METHODS: Patients were identified through an institution-wide computer-based QT screening system which alerts the physician if the QTc ≥ 500 ms. ECGs were retrospectively analyzed with an automated T-wave analysis program. Congenital LQTS was compared in a 1:3 ratio to those with an identified acquired etiology for QT prolongation (electrolyte abnormality and/or prescription of known QT prolongation medications). Linear discriminant analysis was performed using 10-fold cross-validation to statistically test the selected features. RESULTS: The 12-lead ECG of 38 patients with congenital LQTS and 114 patients with drug-induced and/or electrolyte-mediated QT prolongation were analyzed. In lead V5 , patients with acquired QT prolongation had a shallower T wave right slope (-2,322 vs. -3,593 mV/s), greater T-peak-Tend interval (109 vs. 92 ms), and smaller T wave center of gravity on the x axis (290 ms vs. 310 ms; p < .001). These features could distinguish congenital from acquired causes in 77% of cases (sensitivity 90%, specificity 58%). CONCLUSION: T-wave morphological analysis on lead V5 of the surface ECG could successfully differentiate congenital from acquired causes of QT prolongation.

Noninvasive blood potassium measurement using signal-processed, single-lead ecg acquired from a handheld smartphone.

OBJECTIVE: We have previously used a 12-lead, signal-processed ECG to calculate blood potassium levels. We now assess the feasibility of doing so with a smartphone-enabled single lead, to permit remote monitoring. PATIENTS AND METHODS: Twenty-one hemodialysis patients held a smartphone equipped with inexpensive FDA-approved electrodes for three 2min intervals during hemodialysis. Individualized potassium estimation models were generated for each patient. ECG-calculated potassium values were compared to blood potassium results at subsequent visits to evaluate the accuracy of the potassium estimation models. RESULTS: The mean absolute error between the estimated potassium and blood potassium 0.38±0.32 mEq/L (9% of average potassium level) decreasing to 0.6 mEq/L using predictors of poor signal. CONCLUSIONS: A single-lead ECG acquired using electrodes attached to a smartphone device can be processed to calculate the serum potassium with an error of 9% in patients undergoing hemodialysis. SUMMARY: A single-lead ECG acquired using electrodes attached to a smartphone can be processed to calculate the serum potassium in patients undergoing hemodialysis remotely.

Electrocardiographic Predictors of Torsadogenic Risk During Dofetilide or Sotalol Initiation: Utility of a Novel T Wave Analysis Program.

INTRODUCTION: Initiation of class III anti-arrhythmic medications requires telemetric monitoring for ventricular arrhythmias and QT prolongation to reduce the risk of torsades de pointes (TdP). Heart rate-corrected QT interval (QTc) is an indicator of risk, however it is imperfect, and subtle abnormalities of repolarization have been linked with arrhythmogenesis. PURPOSE: Identification of electrocardiographic predictors of torsadogenic risk through the application of a novel T wave analysis tool. METHODS: Among all patients admitted to Mayo Clinic for initiation of dofetilide or sotalol, we identified 13 cases who developed drug-induced TdP and 26 age and sex matched controls that did not develop TdP. The immediate pre-TdP ECG of those with TdP was compared to the last ECG performed prior to hospital discharge in controls using a novel T wave program that quantified subtle changes in T wave morphology. RESULTS: The QTc and 12 T wave parameters successfully distinguished TdP cases from controls. The top performing parameters were the QTc in lead V3 (mean case vs control 480 vs 420 msec, p < 0.001, r = 0.72) and T wave right slope in lead I (mean case vs control -840.29 vs -1668.71 mV/s, p = 0.002, r = 0.45). The addition of T wave right slope to QTc improved prediction accuracy from 79 to 88 %. CONCLUSION: Our data demonstrate that, in addition to QTc, the T wave right slope is correlated strongly with TdP risk. This suggests that a computer-based repolarization measurement tool that integrates additional data beyond the QTc may identify patients with the greatest torsadogenic potential.

Noninvasive potassium determination using a mathematically processed ECG: proof of concept for a novel "blood-less, blood test".

OBJECTIVE: To determine if ECG repolarization measures can be used to detect small changes in serum potassium levels in hemodialysis patients. PATIENTS AND METHODS: Signal-averaged ECGs were obtained from standard ECG leads in 12 patients before, during, and after dialysis. Based on physiological considerations, five repolarization-related ECG measures were chosen and automatically extracted for analysis: the slope of the T wave downstroke (T right slope), the amplitude of the T wave (T amplitude), the center of gravity (COG) of the T wave (T COG), the ratio of the amplitude of the T wave to amplitude of the R wave (T/R amplitude), and the center of gravity of the last 25% of the area under the T wave curve (T4 COG) (Fig. 1). RESULTS: The correlations with potassium were statistically significant for T right slope (P<0.0001), T COG (P=0.007), T amplitude (P=0.0006) and T/R amplitude (P=0.03), but not T4 COG (P=0.13). Potassium changes as small as 0.2mmol/L were detectable. CONCLUSION: Small changes in blood potassium concentrations, within the normal range, resulted in quantifiable changes in the processed, signal-averaged ECG. This indicates that non-invasive, ECG-based potassium measurement is feasible and suggests that continuous or remote monitoring systems could be developed to detect early potassium deviations among high-risk patients, such as those with cardiovascular and renal diseases. The results of this feasibility study will need to be further confirmed in a larger cohort of patients.

Architectural T-Wave Analysis and Identification of On-Therapy Breakthrough Arrhythmic Risk in Type 1 and Type 2 Long-QT Syndrome.

BACKGROUND: Although the hallmark of long-QT syndrome (LQTS) is abnormal cardiac repolarization, there are varying degrees of phenotypic expression and arrhythmic risk. Our aim was to evaluate the performance of a morphological T-wave analysis program in defining breakthrough LQTS arrhythmic risk beyond the QTc value. METHODS AND RESULTS: We analyzed 407 genetically confirmed patients with LQT1 (n=246; 43% men) and LQT2 (n=161; 41% men) over the mean follow-up period of 6.4±3.9 years. ECG analysis was conducted using a novel, proprietary T-wave analysis program. Time to a LQTS-associated cardiac event was analyzed using Cox proportional hazards regression methods. Twenty-three patients experienced ≥1 defined breakthrough cardiac arrhythmic events with 5- and 10-year event rates of 4% and 7%. Two independent predictors of future LQTS-associated cardiac events from the surface ECG were identified: left slope of T wave in lead V6 (hazard ratio=0.40 [0.24-0.69]; P<0.001) and T-wave center of gravity x axis (last 25% of wave) in lead I (hazard ratio=1.90 [1.21-2.99]; P=0.005), C statistic of 0.77 (0.65-0.89). When added to the QTc (C statistic 0.68 for QTc alone), discrimination improved to 0.78. Genotype analysis showed weaker association between these T-wave variables and LQT1-triggered events while these features were stronger in patients with LQT2 and significantly outperformed the QTc (C statistic, 0.82 [0.71-0.93]). CONCLUSION: Detailed morphological analysis of the T wave provides novel insights into risk of breakthrough arrhythmic events in LQTS, particularly LQT2. This observation has the potential to guide clinical decision making and further refine risk stratification.

Electrocardiographic predictors of coronary microvascular dysfunction in patients with non-obstructive coronary artery disease: Utility of a novel T wave analysis program.

BACKGROUND: Coronary microvascular dysfunction (CMD) is linked to adverse cardiovascular events. Definitive diagnosis of CMD requires invasive provocative testing during angiography. We developed and tested a novel computerized T wave analysis tool to identify electrocardiographic signatures of CMD. METHODS: 1552 patients underwent an invasive assessment of coronary microvascular function. Patients with interpretable pre-procedural ECGs were divided into 2 age and sex matched groups (n=261 in each group, 75% female): normal microvascular function, CFR>2.5 (CFR+), and abnormal microvascular function, CFR ≤ 2.5 (CFR-). ECGs were evaluated using a novel T wave program that quantified subtle changes in T wave morphology. RESULTS: T wave repolarization parameters were significantly different between patients with normal and abnormal microvascular function. The top 3 features in males comprised of T wave area in V6 (CFR+: 10091.4 mV(2) vs. CFR-: 8152.3 mV(2), p<0.05); T1 Y-center of gravity in lead II (CFR+: 17.8 mV vs. CFR-: 22.4, p<0.005) and T Peak-T End in lead II (CFR+: 97.6 msec vs. CFR-: 91.1 msec, p<0.05). These could identify the presence of an abnormal CFR with 74 ± 0.2% accuracy. In females, the top 3 features were T wave right slope lead V6 (CFR+: -2489.1 mV/msec vs. CFR-: -2352.3 mV/msec, p<0.005); Amplitude in V6 (CFR+: 190.4 mV vs. 172.7 mV, p=0.05) and Y-center of gravity in lead V1 (CFR+: 33.3 vs. CFR-: 40.0, p=0.001). These features could identify the presence of an abnormal CFR with 67 ± 0.3% accuracy. CONCLUSION: Our data demonstrates that a computer-based repolarization measurement tool may identify electrocardiographic signatures of CMD.

Identification of Concealed and Manifest Long QT Syndrome Using a Novel T Wave Analysis Program.

BACKGROUND: Congenital long QT syndrome (LQTS) is characterized by QT prolongation. However, the QT interval itself is insufficient for diagnosis, unless the corrected QT interval is repeatedly ≥500 ms without an acquired explanation. Further, the majority of LQTS patients have a corrected QT interval below this threshold, and a significant minority has normal resting corrected QT interval values. Here, we aimed to develop and validate a novel, quantitative T wave morphological analysis program to differentiate LQTS patients from healthy controls. METHODS AND RESULTS: We analyzed a genotyped cohort of 420 patients (22±16 years, 43% male) with either LQT1 (61%) or LQT2 (39%). ECG analysis was conducted using a novel, proprietary T wave analysis program that quantitates subtle changes in T wave morphology. The top 3 discriminating features in each ECG lead were determined and the lead with the best discrimination selected. Classification was performed using a linear discriminant classifier and validated on an untouched cohort. The top 3 features were Tpeak-Tend interval, T wave left slope, and T wave center of gravity x axis (last 25% of the T wave). Lead V6 had the best discrimination. It could distinguish 86.8% of LQTS patients from healthy controls. Moreover, it distinguished 83.33% of patients with concealed LQTS from controls, despite having essentially identical resting corrected QT interval values. CONCLUSIONS: T wave quantitative analysis on the 12-lead surface ECG provides an effective, novel tool to distinguish patients with either LQT1/LQT2 from healthy matched controls. It can provide guidance while mutation-specific genetic testing is in motion for family members.

Novel Bloodless Potassium Determination Using a Signal-Processed Single-Lead ECG.

BACKGROUND: Hyper- and hypokalemia are clinically silent, common in patients with renal or cardiac disease, and are life threatening. A noninvasive, unobtrusive, blood-free method for tracking potassium would be an important clinical advance. METHODS AND RESULTS: Two groups of hemodialysis patients (development group, n=26; validation group, n=19) underwent high-resolution digital ECG recordings and had 2 to 3 blood tests during dialysis. Using advanced signal processing, we developed a personalized regression model for each patient to noninvasively calculate potassium values during the second and third dialysis sessions using only the processed single-channel ECG. In addition, by analyzing the entire development group's first-visit data, we created a global model for all patients that was validated against subsequent sessions in the development group and in a separate validation group. This global model sought to predict potassium, based on the T wave characteristics, with no blood tests required. For the personalized model, we successfully calculated potassium values with an absolute error of 0.36±0.34 mmol/L (or 10% of the measured blood potassium). For the global model, potassium prediction was also accurate, with an absolute error of 0.44±0.47 mmol/L for the training group (or 11% of the measured blood potassium) and 0.5±0.42 for the validation set (or 12% of the measured blood potassium). CONCLUSIONS: The signal-processed ECG derived from a single lead can be used to calculate potassium values with clinically meaningful resolution using a strategy that requires no blood tests. This enables a cost-effective, noninvasive, unobtrusive strategy for potassium assessment that can be used during remote monitoring.